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Knowledge, practices, and environmental health risks associated with electronic waste recycling in Cotonou, Benin

2020· article· en· W3169581351 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueISEE Conference Abstracts · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnvironmental healthElectronic wasteChinaBiomedical wasteBusinessPersonal protective equipmentMedicineEnvironmental protectionGeographyEngineeringWaste managementEconomic growthHealth careCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

The recycling of e-waste is increasing rapidly worldwide and there remain outstanding environmental health concerns. However, most studies are localized to few countries (e.g., China, Ghana). This study analyzes the knowledge and practices of e-waste recyclers in Cotonou from which a deeper understanding of environmental health risks could be determined. A descriptive, cross-sectional study was conducted in September 2018. All e-waste recyclers working in Cotonou, having given their consent and available during the investigation period were interviewed individually. Survey data was collected from 45 recyclers concern their professional profile, knowledge of the risks of their activities on health and environment and their daily recycling practices. The data analysis was done under the SPSS software and the graphs were generated under Microsoft Excel. All of the 45 people were male. The average age is 24 ± 6 years old and 53.3% of recyclers have at least 3 years of seniority. Recyclers dismantle (97.8%), sort (91.1%) and incinerate (88.9%) e-waste. Only 44.2% of recyclers wear at least one piece of personal protective equipment and 48.8% do not wash their hands before eating at recycling sites. More than 90% noted that their residues are abandoned in nature and 46.7% think that e-waste can pollute water against 71.1% for air and soil. Regarding the diseases that can be linked to their activity, recyclers self-recognize respiratory diseases 67.4%, heart diseases 62.8%, eye diseases 65.1%, kidney diseases 41.9% and cancers 30.2%.Note that the number of e-waste dismantled per month is significantly associated with the symptoms experienced: blood in the urine and stool, wounds, dizziness, itchy skin. The number of hours of work per day is associated with: blood in the urine, dizziness, itchy skin and airway obstruction. It becomes important to raise awareness of e-waste workers about the dangers of their activities and encourage prevention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.300
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it